Data Governance from a Strategic Management Perspective

Data Governance from aStrategic Management Perspectiveg g p

Prof. Dr. Boris Otto, Assistant ProfessorBerlin, February 16, 2012

AgendaAgenda

A strategic view of Data Governance A strategic view of Data Governance

Data Governance as dynamic capability

Examples from the consortium

© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 2

AgendaAgenda





Data Governance is necessary to respond to a number Data Governance is necessary to respond to a number of external business requirements

1 Customer-Centric Business Models

$$ Value Chain Excellence

§ Contractual and Regulatory Compliance


Data Governance and Data Quality Management are Data Governance and Data Quality Management are closely interrelated

MaximizeData Quality

MaximizeData Value

is sub-goal of

D D Q li

supports supports

is led by is sub-functionData Governance

Data Quality Management

Data Management

is led by is sub-function

of

Data Assetsare object of are object of

are object of

Data Assets

Legend: Goal Function Data.

Source: Otto, B.: Data Governance, in: WIRTSCHAFTSINFORMATIK, 53, 4, 2011, S. 235-238.


, , , , , ,

Data Governance effectiveness still varies widely todayData Governance effectiveness still varies widely today

25 07.5

7.5

25.0

30 0 very good

30 0

30.0 y g

good

di30.0 mediocre

adequate

poorSource: Messerschmidt, M.; Stüben, J.: Verborgene Schätze: Eine internationale Studie zum Master-Data-Management, PricewaterhouseCooopers AG, 2011, pp. 25 et seq.NB: Figures are percentages.


What issues does upper management see with regard What issues does upper management see with regard to Data Governance? The case of Syngenta

Business benefitsus ess be e ts– “Keep in mind to balance costs for double-handling on one hand and of high

discipline on the other.”“Emphasize usability of MDM its value ”– Emphasize usability of MDM, its value.

Organizational readinessg– “Data owners and data stewards are terms people don‘t understand. Be

educational and promotive.”– “Organizational maturity differs in the divisions ”Organizational maturity differs in the divisions.

Data Governance implementation and execution– “What’s the migration path? Are there intermediate staging gates?”– “Is it a journey or can one make a choice? Or both?”– “How to integrate this strategy into the program of next year?”How to integrate this strategy into the program of next year?– “How to integrate the 35,000 ft view with daily operations?”

NB: Selected quotes from a series of eight interviews with line managers conducted in October and November 2011.


q g g

AgendaAgenda





Data Governance, what is it really?Data Governance, what is it really?

… a business process?

an organizational unit?… an organizational unit?

… just a persistent management fad?


Data Governance as a Dynamic CapabilityData Governance as a Dynamic Capability

Dynamic capabilities describe an enterprise’s ability to address a changing market environment by integrating, reconfiguring, gaining, and releasing resources1

1) Eisenhardt K M and Martin J A Dynamic Capabilities: What Are They? Strategic Management Journal 21 10-11 (2000) 1105-1121


1) Eisenhardt, K.M. and Martin, J.A. Dynamic Capabilities: What Are They? Strategic Management Journal, 21, 10-11 (2000), 1105-1121.

Companies start from different Data Management Companies start from different Data Management positions

2

2

1 5

6

D t Q lit M t

Data Strategy Management

2

2

1

6

5

Data Stewardship

Data Quality Management

1 2 3 2Data Lifecycle Management

1 2 1 4Data Architecture Management

7 1Database Operations Management

Does not exist in comprehensive formExisted prior to Data Governance in similar form

Absolute numbers.Existed prior to Data Governance in similar formExisted prior to Data Governance, but was significantly revised or extendedNewly created

NB: Based on data from eight cases (Bayer CropScience, Corning Cable Systems, DB Netz, Deutsche Telekom, Johnson & Johnson, Robert B h S t ZF)


Bosch, Syngenta, ZF)

Note taken in a meeting with Johnson & Johnson Note taken in a meeting with Johnson & Johnson on November 29, 2011, in Skillman, NJ


The ideal lifecycle of Data Governance as a dynamic The ideal lifecycle of Data Governance as a dynamic capability resembles an “S” curve

EFounding Phase „First Time Right“ Cleansing

E

Legend: E − Effectiveness; A − Amount of Activity. A


AgendaAgenda





It is not a perfect world, thoughIt is not a perfect world, though

E E E

3.

Pattern I Pattern II

4.

5. Pattern IIIE E E

2.1.

2.3.

1. 2.3. 4.

2008 2009 2010 2011

1.

2008 2009 2010 2011 2007 2008 2009 2010

A A A

2008 2009 2010 2011

1. CDM unit launched2. Data creation workflow3. DQ metrics launched

2008 2009 2010 2011

1. DG project launched2. Address to board3. DQ metrics launched4. „Community“ approach

2007 2008 2009 2010

1. CDM unit launched2. Progress report to the board

proposed3. Inventory data quality4. „Community approach

proposed5. DG council launched

3. Inventory data quality assessment

4. CDM reorganized

Legend: E − Effectiveness; A − Amount of Activity; CDM − Corporate Data Management; DQ − Data Quality; DG −Data Governance.


Double-loop learning is a central success factor for Double loop learning is a central success factor for Data Governance maturity

“Problems cannot be solved by the same level of thinkingby the same level of thinking that created them.”


“We really need to change the way Data Governance and Data Management are perceived throughout the companyData Management are perceived throughout the company. Usually people do not welcome me with open arms when I

enter their office Data management is somehow treated likeenter their office. Data management is somehow treated like a skunk—no-one wants to spend too much time with it. It would be very important that we change the image of thewould be very important that we change the image of the

issue. What would have a more positive connotation than a skunk? Maybe a squirrel!” Karl-Heinz Weber, Bayer CropScience AGskunk? Maybe a squirrel! Karl Heinz Weber, Bayer CropScience AG


Options for novel approaches: The case of Corning Options for novel approaches: The case of Corning Cable Systems

1 Align MDM with the company’s culture of quality management Proof of concept for customer master data creation in NAFTA

customer master life cycle

TransferringTQM principles to MDM

1

Master data as an asset Establish business-oriented data quality metrics Data life cycle / Retirement process

Managing cost andvalue of master data

2

Data life cycle / Retirement process

Buy-in for MDM organization from data owners still missing Continuous roll-out of roles and responsibilities in MDMGlobal

Data Governance rollout

3

Implementation of a shared MDM ServiceData Governance rollout

Knowledge capitalization on an organization and system levelGlobal leveraging of 4

g p g y Foundation of a global center for excellence

g gknowledge assets

5

Technical integration/substitution of ASI, Windchill with SAP Extend workflow from material master to other domains

System integration and process automation


Existing Data Management expertise is a crucial factor for identifying a Existing Data Management expertise is a crucial factor for identifying a need for action during the establishment of Data Governance


The maturity model is an instrument for controllingThe maturity model is an instrument for controllingData Governance effectiveness

StrategyControlling

Organization

Applications

DataProcesses& Methods

DataArchitecture

Legend: Current value 2010Target value 2011 (= one maturity level for all enablers)


RWE benefits from EUR 2.6 million costs avoidedRWE benefits from EUR 2.6 million costs avoided

Average cost rate Number of Number of Value = per master data

record*X ( duplicate

records+ new records

avoided)

Backward Forward

* 2,861 EUR (2006).Source: Holzapfel, T. Harmonisierung Von Materialstammdaten - Eine Herausforderung. Proceedings of the Stammdaten-Management Forum (Frankfurt 2007 09 27) IIR Deutschland 2007


Forum (Frankfurt, 2007-09-27). IIR Deutschland, 2007.

Some more tangible benefit examplesSome more tangible benefit examples

Savings of 2 percent of actual value in stockSavings of 2 percent of actual value in stock

More than GBP 500 million saved through retrieval of “lost assets”More than GBP 500 million saved through retrieval of “lost assets”

CHF 3,000 saved per obsolete master data recordCHF 3,000 saved per obsolete master data record


Contact DetailsContact Details

Prof. Dr. Boris OttoAssistant ProfessorUniversity of St. [email protected].: +41 71 224 32 20

http://cdq.iwi.unisg.ch


Data Governance from a Strategic Management Perspective

Business

Transcript of Data Governance from a Strategic Management Perspective